✨SIMS-V: Simulated Instruction-Tuning for Spatial Video Understanding

📝 Summary:
SIMS-V uses 3D simulators to generate diverse spatial video training data. This efficiently trains multimodal language models, overcoming real-world data bottlenecks. A 7B model trained on this simulated data significantly outperforms larger baselines on real-world spatial reasoning tasks.

🔹 Publication Date: Published on Nov 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.04668
• PDF: https://arxiv.org/pdf/2511.04668
• Github: https://ellisbrown.github.io/sims-v/

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#SpatialAI #MultimodalLLM #SimulatedData #ComputerVision #DeepLearning

✨Visual Spatial Tuning

📝 Summary:
Visual Spatial Tuning VST is a framework that progressively trains Vision-Language Models VLMs using specialized datasets VST-P for spatial perception and VST-R for reasoning. VST achieves state-of-the-art results on spatial benchmarks without harming general VLM capabilities, leading to more phy...

🔹 Publication Date: Published on Nov 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.05491
• PDF: https://arxiv.org/pdf/2511.05491
• Project Page: https://yangr116.github.io/vst_project/
• Github: https://github.com/Yangr116/VST

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#VisionLanguageModels #SpatialAI #ComputerVision #DeepLearning #AIResearch

✨Reasoning via Video: The First Evaluation of Video Models' Reasoning Abilities through Maze-Solving Tasks

📝 Summary:
VR-Bench evaluates video models' spatial reasoning using maze-solving tasks. It demonstrates that video models excel in spatial perception and reasoning, outperforming VLMs, and benefit from diverse sampling during inference. These findings show the strong potential of reasoning via video for spa...

🔹 Publication Date: Published on Nov 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.15065
• PDF: https://arxiv.org/pdf/2511.15065
• Project Page: https://imyangc7.github.io/VRBench_Web/
• Github: https://github.com/ImYangC7/VR-Bench

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#VideoModels #AIReasoning #SpatialAI #ComputerVision #MachineLearning

✨Think3D: Thinking with Space for Spatial Reasoning

📝 Summary:
Think3D improves vision-language models' 3D reasoning by enabling interactive spatial exploration using 3D reconstruction and camera operations. This training-free framework significantly boosts performance on spatial reasoning tasks for models like GPT-4.1 and Gemini 2.5 Pro, offering a path to ...

🔹 Publication Date: Published on Jan 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13029
• PDF: https://arxiv.org/pdf/2601.13029
• Github: https://github.com/zhangzaibin/spagent

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#3DReasoning #SpatialAI #VisionLanguageModels #MachineLearning #ComputerVision

✨IVRA: Improving Visual-Token Relations for Robot Action Policy with Training-Free Hint-Based Guidance

📝 Summary:
IVRA improves spatial understanding in VLA models by training-free injection of vision encoder affinity signals into language model layers at inference time. This enhances geometric structure and robot action policies. It shows consistent performance gains across diverse 2D and 3D manipulation ta...

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16207
• PDF: https://arxiv.org/pdf/2601.16207
• Github: https://jongwoopark7978.github.io/IVRA

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#Robotics #VisionLanguageModels #SpatialAI #RobotLearning #DeepLearning

✨Enhancing Spatial Understanding in Image Generation via Reward Modeling

📝 Summary:
Text-to-image models struggle with complex spatial relationships. This paper introduces SpatialScore, a reward model trained on 80k preference pairs, to evaluate and improve spatial accuracy. It significantly enhances spatial understanding in image generation via reinforcement learning.

🔹 Publication Date: Published on Feb 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.24233
• PDF: https://arxiv.org/pdf/2602.24233
• Project Page: https://dagroup-pku.github.io/SpatialT2I/
• Github: https://github.com/DAGroup-PKU/SpatialT2I

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#ImageGeneration #TextToImage #SpatialAI #RewardModeling #DeepLearning

✨Communicating about Space: Language-Mediated Spatial Integration Across Partial Views

📝 Summary:
MLLMs struggle with collaborative spatial communication and building shared mental models from partial views. The COSMIC benchmark shows MLLMs achieve only 72 percent accuracy compared to humans 95 percent, performing poorly on relational reasoning and global map building. Models fail to converge...

🔹 Publication Date: Published on Mar 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.27183
• PDF: https://arxiv.org/pdf/2603.27183
• Github: https://github.com/ankursikarwar/Cosmic

✨ Datasets citing this paper:
• https://huggingface.co/datasets/mair-lab/Cosmic

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#MLLMs #SpatialAI #AIResearch #HumanAICollaboration #ComputerVision

✨CityRAG: Stepping Into a City via Spatially-Grounded Video Generation

📝 Summary:
CityRAG generates long-term, physically grounded video sequences that maintain environmental consistency and support complex navigation through real-world geography using geo-registered data as contex...

🔹 Publication Date: Published on Apr 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19741
• PDF: https://arxiv.org/pdf/2604.19741
• Project Page: https://cityrag.github.io/

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#VideoGeneration #GenerativeAI #SpatialAI #ComputerVision #UrbanSimulation